The chronic inflammatory state that accompanies obesity is a major contributor to insulin resistance and other dysfunctional adaptations in adipose tissue. Cellular and secreted factors promote the inflammatory milieu of obesity, but the transcriptional pathways that drive these processes are not well described. Although the canonical inflammatory transcription factor NF-κB is considered to be the major driver of adipocyte inflammation, members of the interferon regulatory factor (IRF) family may also play a role in this process. Here, we determined that IRF3 expression is upregulated in the adipocytes of obese mice and humans. Signaling through TLR3 and TLR4, which lie upstream of IRF3, induced insulin resistance in murine adipocytes, while IRF3 knockdown prevented insulin resistance. Furthermore, improved insulin sensitivity in IRF3-deficient mice was associated with reductions in intra-adipose and systemic inflammation in the high fat–fed state, enhanced browning of subcutaneous fat, and increased adipose expression of GLUT4. Taken together, the data indicate that IRF3 is a major transcriptional regulator of adipose inflammation and is involved in maintaining systemic glucose and energy homeostasis.
Manju Kumari, Xun Wang, Louise Lantier, Anna Lyubetskaya, Jun Eguchi, Sona Kang, Danielle Tenen, Hyun Cheol Roh, Xingxing Kong, Lawrence Kazak, Rasheed Ahmad, Evan D. Rosen
Usage data is cumulative from March 2020 through March 2021.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.